A visual attention model for adapting images on small displays

Li-Qun Chen, Xing Xie, Xin Fan, Wei-Ying Ma, Hong-Jiang Zhang, He-Qin Zhou

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

377 Citations (Scopus)

Abstract

Image adaptation, one of the essential problems in adaptive content delivery for universal access, has been actively explored for some time. Most existing approaches have focused on generic adaptation with a view to saving file size under constraints in client environment and have hardly paid attention to user perceptions of the adapted result. Meanwhile, the major limitation on the user's delivery context is moving away from data volume (or time-to-wait) to screen size because of the galloping development of hardware technologies. In this paper, we propose a novel method for adapting images based on user attention. A generic and extensible image attention model is introduced based on three attributes (region of interest, attention value, and minimal perceptible size) associated with each attention object. A set of automatic modeling methods are presented to support this approach. A branch-and-bound algorithm is also developed to find the optimal adaptation efficiently. Experimental results demonstrate the usefulness of the proposed scheme and its potential application in the future.
Original languageEnglish
Pages (from-to)353-364
JournalMultimedia Systems
Volume9
Issue number4
DOIs
Publication statusPublished - Oct 2003
Externally publishedYes

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Research Keywords

  • Attention model
  • Attention value
  • Image adaptation
  • Information fidelity
  • Minimal perceptible size
  • Region-of-interest

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